e-Dairy: a dynamic and stochastic whole-farm model that predicts biophysical and economic performance of grazing dairy systems

A whole-farm, stochastic and dynamic simulation model was developed to predict biophysical and economic performance of grazing dairy systems. Several whole-farm models simulate grazing dairy systems, but most of them work at a herd level. This model, named e-Dairy, differs from the few models that work at an animal level, because it allows stochastic behaviour of the genetic merit of individual cows for several traits, namely, yields of milk, fat and protein, live weight (LW) and body condition score (BCS) within a whole-farm model. This model accounts for genetic differences between cows, is sensitive to genotype x environment interactions at an animal level and allows pasture growth, milk and supplements price to behave stochastically. The model includes an energy-based animal module that predicts intake at grazing, mammary gland functioning and body lipid change. This whole-farm model simulates a 365-day period for individual cows within a herd, with cow parameters randomly generated on the basis of the mean parameter values, defined as input and variance and co-variances from experimental data sets. The main inputs of e-Dairy are farm area, use of land, type of pasture, type of crops, monthly pasture growth rate, supplements offered, nutritional quality of feeds, herd description including herd size, age structure, calving pattern, BCS and LW at calving, probabilities of pregnancy, average genetic merit and economic values for items of income and costs. The model allows to set management policies to define: dry-off cows (ceasing of lactation), target pre- and post-grazing herbage mass and feed supplementation. The main outputs are herbage dry matter intake, annual pasture utilisation, milk yield, changes in BCS and LW, economic farm profit and return on assets. The model showed satisfactory accuracy of prediction when validated against two data sets from farmlet system experiments. Relative prediction errors were <10% for all variables, and concordance correlation coefficients over 0.80 for annual pasture utilisation, yields of milk and milk solids (MS; fat plus protein), and of 0.69 and 0.48 for LW and BCS, respectively. A simulation of two contrasting dairy systems is presented to show the practical use of the model. The model can be used to explore the effects of feeding level and genetic merit and their interactions for grazing dairy systems, evaluating the trade-offs between profit and the associated risk.